To circumvent this, we use a Matlab implementation of Goodman and
Weare's affine-invariant ensemble MCMC (GWMCMC) [1]},[2]}.
Briefly, this implementation uses a parallel ensemble of MCMC chains
(called “walkers”). New proposals are drawn along the line connecting
two walkers in parameter space. By expanding the space of allowable
directions to draw new samples, GWMCMC mitigates the problem of transition
probabilities getting caught in corners. Moreover, GWMCMC does not
require a researcher to designate the scale of perturbations, since
the magnitude of perturbations scales dynamically as a function of
the distance between walkers in parameter space. In effect, GWMCMC
permits efficient sampling without a priori knowledge of the scales
of the parameters.
